The impact of biased experts in the aggregation of fuzzy preference relations

The group decision making process usually contains a consensus model which focuses on achieving some level of agreement amongst experts before their preferences are aggregated and an alternative is chosen. These consensus models look at the distances between the experts’ preferences and use this to provide each expert with a consensus level. If an expert’s consensus level is below a predefined threshold, the expert will be given recommendations to update certain preferences until they receive a consensus level that is considered acceptable when compared to the group. This report considers situations where the current methods for calculating the consensus between experts can be exploited by a biased expert and then explores some of the ways to reduce the impact of such situations.